Wafer polishing using chemical mechanical planarization (CMP), as shown with reference to FIG. 1, is a key nanoscale manufacturing process that can significantly impact critical requirements facing the semiconductor device manufacturing procedure. Some of these requirements for nanoscale manufacturing include continual feature size reduction, introduction of new materials for higher processing speeds and improved reliability, multilevel metallization (MLM) or interconnections, and increased productivity through larger wafer sizes. The CMP task has been made more challenging in recent years by complex wafer topographies, and the introduction of copper, as a substitute for aluminum, and low-k dielectrics. Some of the difficult manufacturing challenges of CMP include defects identification, such as delamination, dishing, and erosion, end point detection (EPD) and process control.
End point detection (EPD) is the determination of the end of polishing in a chemical mechanical planarization (CMP) process. FIG. 2 illustrates the CMP process and its associated end point as known in the art. If the end point is not detected properly, a defect in the chemical mechanical planarization process for metals, oxides, or dielectrics, known as over and underpolishing, may result. One primary reason for this defect may be the change in material removal rate (MRR) often caused by normal polish pad life cycle, variations in the slurry, variations in the polishing pad, and conditioning issues of pads. Other reasons for over and under polishing may include approximations of empirical MRR calculations and fluctuations in incoming oxide or metal layer thickness. Accordingly, EPD of CMP is a critical operational issue.
Literature in the field of EPD and CMP cites the need for accurate end point detection of a chemical mechanical planarization process involved in three different processes of wafer fabrication, including copper damascene, shallow trench isolation (STI), and interlevel dielectrics (ILD). Some of the challenges known in the art for EPD include: 1) inaccessibility to the entire wafer surface for measurements during polishing; 2) high cost of metrology; 3) difficulty in implementing online methodologies; 4) inaccurate interpretation of in-situ sensor data; and 5) lack of robustness of the detection methodology. Current approaches to EPD are include the analysis of both offline and in-situ sensor data. Offline methods are referred to as dry methods, and include processes in which the wafer is inspected under a microscope to determine its polishing status. Though this method has the advantage of a thorough microscopic level analysis, it is not conducive to higher productivity because the planarization process must be stopped to evaluate the wafer. Additionally, offline methods are expensive due to their cost of ownership.
The in-situ sensor methods known in the art, also referred to as wet methods, include optical, thermal, electric, electrochemical and acoustic emission sensor systems. Optical sensor-based methods known in the art employ interferometry, reflectance and spectral reflectivity, and ellipsometry to acquire thickness measurements. In these methods, a beam of light is passed through the wafer and the wavelength of light emitted from the wafer surface is measured. The wavelength is then used to evaluate the thickness of the wafer and, in turn, detect the end point of polishing. This method becomes inefficient, especially with metal CMP, as the wafer thickness grows. Cu, for example, is optically transparent to only about 30 nm. On patterned ILD wafers, optical methods present additional challenges, such as diffraction, which significantly affects the spectral analysis. Environmental factors such as sensing through air, slurry, and glass during in-situ measurements also affects the performance of optical methods for end point detection currently known in the art.
Thermal systems for end point detection in CMP utilize infrared temperature measurements and changes in temperature to detect an end point. In these thermal systems known in the art, a change in temperature can result from either the change in friction of the wear mechanisms or in the underlying chemical reactions. The major disadvantage of thermal methods for EPD is difficulty in implementation. Implementation is difficult because the infrared sensors have to be fixed onto a transparent pad or be positioned to rotate with the carrier to be able to accurately detect the temperature change. This configuration is difficult to implement in the manufacturing process. Additionally, small changes in temperature values that are difficult to detect, such as those often caused by the presence of thermally diffusive materials, present a significant challenge to thermal EPD detection systems.
Friction based methods for EPD in CMP use motor-current sensing techniques. These techniques are also highly dependent on process parameters and consumables, and become inefficient for polishing ILD, in which there is no transition to an underlying layer with a different coefficient of friction.
Monitoring the material removal rate (MRR) in the CPD process is another alternative for EPD. In this method, an x-ray beam is directed on the downstream slurry and a detector monitors the induced fluorescence. The fluorescence indicates the density of abrasive in the slurry, which is then used in MRR calculations. Though in principal this method works, it has been proven to be ineffective.
Electrochemical methods for EPD measure the electrochemical potential between a measurement electrode, which is either the surface being polished or a probe inserted into the slurry near the wafer, and the reference electrode.
Another approach to EPD in CMP is chemical EPD, which is suitable for polishing wafers with nitride in the second layer. The detection procedure relies on measuring the concentration of nitrous oxide emitted when the end point is reached.
Acoustic emission (AE) and coefficient of friction (CoF) sensors are known in the art to be used in process monitoring for EPD by measuring various properties including the amplitude of the emitted signal, and the frequency of the spectral peaks. Since these properties differ between materials, they can be used to detect transitions from one layer to another during CMP. The presence of noise and the need for advanced signal processing has kept these approaches from being commercially implemented.
Efficient EPD in CMP has been an open research issue since the introduction of CMP to the wafer fabrication process. Several approaches have been proposed in the literature of which only a few rely upon the signals (AE and CoF) obtained directly from the molecular interactions of the polishing process. However, these signals by themselves cannot characterize important process events, like end point. Accordingly, what is needed in the art is an improved end point detection methodology for CMP that is robust and efficient and also capable of real-time implementation.